Cleaning control method and system of photovoltaic cleaning robot

Abstract

The invention discloses a cleaning control method and system of a photovoltaic cleaning robot, and relates to the field of photovoltaic equipment maintenance. The method is applied to a photovoltaic cleaning robot control system and comprises the steps that optical testing is conducted on a target photovoltaic surface, an optical distribution diagram is shot and extracted, and the optical distribution diagram comprises the light transmittance and the refractive index of the target photovoltaic surface; according to the optical distribution diagram, determining ice layer thickness distribution on the surface of the target photovoltaic surface; based on the ice layer thickness distribution, an ice melting strategy of the photovoltaic cleaning robot is determined, and the ice melting strategy comprises ice melting temperature and an ice melting path; and controlling the photovoltaic cleaning machine to clean the target photovoltaic surface according to the ice melting strategy. By implementing the technical scheme provided by the invention, the problem that the photovoltaic module may be damaged in the cleaning process due to the fragile material of the photovoltaic module is solved.

Description

Technical Field

[0001] This application relates to the technical field of photovoltaic equipment maintenance, and specifically relates to a cleaning control method and system for a photovoltaic cleaning robot. Background Art

[0002] In the current operation and maintenance of photovoltaic power stations, photovoltaic cleaning robots play a crucial role. They can efficiently and accurately perform daily cleaning on photovoltaic modules, not only ensuring that the photovoltaic modules maintain good power generation performance but also greatly reducing labor costs.

[0003] Currently, under extremely cold weather conditions, ice layers are extremely likely to condense on the surface of photovoltaic modules. If there are already dirt, dust, bird droppings and other contaminants on the surface of the modules before the formation of the ice layer, then these contaminants will be solidified under the ice layer together. Usually, to remove this ice layer containing contaminants, hot water cleaning is often chosen. However, the material of photovoltaic modules is relatively fragile, and hot water will generate a thermal shock effect on the photovoltaic surface during the cleaning process, and this stimulation is very likely to cause the photovoltaic surface to break, thereby causing serious damage to the photovoltaic modules. Summary of the Invention

[0004] Aiming at the problem that the cleaning process may cause damage to photovoltaic modules due to their fragile material, this application provides a cleaning control method and system for a photovoltaic cleaning robot.

[0005] In a first aspect, this application provides a cleaning control method for a photovoltaic cleaning robot, which is applied to a photovoltaic cleaning robot control system. The method includes: Conduct an optical test on the target photovoltaic surface, and take and extract an optical distribution map, where the optical distribution map includes the transmittance and refractive index of the target photovoltaic surface; Determine the ice layer thickness distribution on the surface of the target photovoltaic surface according to the optical distribution map; Based on the ice layer thickness distribution, determine the ice melting strategy of the photovoltaic cleaning robot, where the ice melting strategy includes the ice melting temperature and the ice melting path; Control the photovoltaic cleaning robot to clean the target photovoltaic surface according to the ice melting strategy.

[0006] Optionally, the determining the ice layer thickness distribution on the surface of the target photovoltaic surface according to the optical distribution map specifically includes: Convert the optical distribution map into an optical image matrix, which is composed of multiple pixel points, and each pixel point stores its corresponding refractive index and transmittance; Calculate the refractive index ice layer thickness and the transmittance ice layer thickness of the optical image matrix; Calculate the ice layer thickness difference between the refractive index ice layer thickness and the transmittance ice layer thickness; Determine whether the ice layer thickness difference is less than or equal to the ice layer thickness difference threshold; If the ice layer thickness difference is less than or equal to the ice layer thickness difference threshold, select either the refractive index ice layer thickness or the light transmittance ice layer thickness as the ice layer thickness distribution of the target photovoltaic surface.

[0007] Optionally, determining whether the ice layer thickness difference is less than or equal to the ice layer thickness difference threshold further includes: If the ice layer thickness difference is greater than the ice layer thickness difference threshold, convert the optical image matrix into a gradient matrix, where the gradient matrix is composed of multiple gradient points, and one gradient point corresponds to one pixel point; Calculate the light transmittance gradient vectors and refractive index gradient vectors of multiple gradient points; Calculate the light transmittance - refractive index correlation coefficients corresponding to multiple gradient points according to the light transmittance gradient vectors and refractive index gradient vectors of multiple gradient points; Use a weight function to calculate the ice layer thickness weight coefficients of multiple gradient points; Based on the light transmittance - refractive index correlation coefficients and ice layer thickness weight coefficients corresponding to multiple gradient points, calculate the ice layer thickness distribution on the surface of the target photovoltaic surface, where the ice layer thickness distribution includes the ice layer thicknesses corresponding to multiple gradient points respectively.

[0008] Optionally, the weight function is specifically: Wherein, is the weight coefficient of the gradient point at the x - th row and y - th column in the gradient matrix, r(x, y) is the correlation coefficient between the refractive index gradient vector and the light transmittance gradient vector within the gradient point at the x - th row and y - th column in the gradient matrix, , , and are respectively the refractive index horizontal gradient value, refractive index vertical gradient value, light transmittance horizontal gradient value, and light transmittance vertical gradient value of the gradient point at the x - th row and y - th column in the gradient matrix.

[0009] Optionally, determining the ice melting strategy of the photovoltaic cleaning robot based on the ice layer thickness distribution specifically includes: Determine the path weights of multiple gradient points according to the physical heights of multiple gradient points from the ground; Multiply the path weights of multiple gradient points by their respective corresponding ice layer thicknesses to determine the ice melting priorities of multiple gradient points; Determine the ice melting path according to the ice melting priorities of multiple gradient points.

[0010] Optionally, determining the ice melting strategy of the photovoltaic cleaning robot based on the ice layer thickness distribution specifically further includes: Calculate the required ice melting heat of multiple gradient points according to the ice layer thickness of multiple gradient points; According to the ambient temperature and the required ice melting heat of multiple gradient points, use the heat balance conduction formula to calculate the ice melting temperature of multiple gradient points.

[0011] Optionally, after controlling the photovoltaic cleaning machine to clean the target photovoltaic surface, it further includes: When the ice layer on the target photovoltaic surface melts, update the component table in the atomized water vapor, and the component table contains the decontamination components of stubborn stains.

[0012] In a second aspect, the present application provides a cleaning control system for a photovoltaic cleaning robot. The system is a control system for a photovoltaic cleaning robot, including a test module, a processing module, and a control module, where: The test module is used to perform optical tests on the target photovoltaic surface and capture and extract an optical distribution map, and the optical distribution map contains the light transmittance and refractive index of the target photovoltaic surface; The processing module is used to determine the ice layer thickness distribution on the surface of the target photovoltaic surface according to the optical distribution map; based on the ice layer thickness distribution, determine the ice melting strategy of the photovoltaic cleaning robot, and the ice melting strategy includes the ice melting temperature and the ice melting path; The control module is used to control the photovoltaic cleaning machine to clean the target photovoltaic surface according to the ice melting strategy.

[0013] In a third aspect, the present application provides an electronic device, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method described in any one of the first aspects.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, and the computer-readable storage medium stores instructions. When the instructions are executed, the method described in any one of the first aspects is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through comprehensive optical testing of the target photovoltaic surface, this application obtains an optical distribution map including transmittance and refractive index, and accurately analyzes and determines the ice layer thickness based on this. Then, based on the ice layer thickness, the appropriate atomized water vapor melting temperature and melting path are calculated. At this time, the photovoltaic cleaning robot is controlled to adjust the atomized water vapor temperature and spray it onto the target photovoltaic surface according to the melting path, which can not only ensure the efficient melting of the ice layer, but also avoid the risk of thermal shock, thus effectively preventing damage to the photovoltaic modules. Finally, combined with subsequent flushing and cleaning, the ice layer and the dirt under the ice layer can be completely removed, restoring the best light transmittance performance of the photovoltaic modules, maintaining good power generation efficiency, reducing corrosion and damage caused by dirt accumulation, and extending its service life. In addition, the whole process is highly automated, greatly reducing manual operation, lowering labor costs and safety risks, while avoiding component damage caused by improper cleaning, reducing maintenance and replacement costs, and comprehensively improving the economic benefits and operation and maintenance efficiency of the photovoltaic power station.

[0016] 2. When calculating the ice layer thickness on the surface of the target photovoltaic surface, since the ice layer is not pure and often contains impurities (salts, dust) in the environment, when the impurity content is high, the ice layer thickness calculated by the Beer-Lambert law is not accurate. Therefore, this application calculates the refractive index ice layer thickness and the transmittance ice layer thickness respectively, and then compares the difference between the two. If the difference is small, it indicates that the ice layer has a high purity, and the calculated ice layer thickness is relatively accurate. If the difference is large, a gradient analysis is performed on the optical distribution map of the ice layer. By calculating the correlation between the refractive index and the transmittance at the gradient point, the change trend of the two at this gradient point is determined. If the change regions of the two are highly consistent, it indicates that the calculated ice layer thickness at this gradient point has high reliability, so a higher weight coefficient is assigned. On the contrary, a lower weight coefficient is assigned. Finally, according to the weight coefficients of each gradient point, the ice layer thickness distribution of the entire ice layer is calculated to simulate the effect of screening out impurities in the ice layer, making the finally calculated ice layer thickness of each gradient point more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic flowchart of a cleaning control method for a photovoltaic cleaning robot provided by an embodiment of this application.

[0018] Figure 2 is a schematic structural diagram of a cleaning control system for a photovoltaic cleaning robot provided by an embodiment of this application.

[0019] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of this application.

[0020] Explanation of the reference numerals: 1. Test module; 2. Processing module; 3. Control module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. DETAILED DESCRIPTION

[0021] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0022] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0023] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0024] In today's large-scale photovoltaic power station operation and maintenance system, photovoltaic cleaning robots have become a key link in ensuring the efficient and stable operation of power stations. With advanced sensing technology and precise mechanical structure, they can efficiently and accurately carry out daily cleaning work on photovoltaic modules. This automated cleaning process not only effectively ensures that photovoltaic modules always maintain good power generation performance, greatly improves power generation efficiency, and creates stable power output for power stations, but also greatly reduces labor cost investment, freeing manpower from the heavy and high-risk photovoltaic module cleaning work and investing in more technical and creative operation and maintenance links. However, under extremely cold weather conditions, ice layers are extremely likely to condense on the surface of photovoltaic modules. If there are already dirt such as stains, dust, and bird droppings on the module surface before the ice layer forms, then these dirt will be solidified together under the ice layer, resulting in increased cleaning difficulty. To address this situation, hot water is sprayed for cleaning during washing. Although this cleaning method is relatively simple and effective, since the material of the photovoltaic module is relatively fragile, the hot water will generate a thermal shock effect on the cleaning surface during the cleaning process. This stimulation is very likely to cause the photovoltaic surface to break, and then cause serious damage to the photovoltaic module.

[0025] To solve the above problems, the present application provides a cleaning control method for a photovoltaic cleaning robot. This method is applied to a photovoltaic robot control system, as Figure 1 shown. This method includes steps S101 to S104, and the above steps are as follows: S101. Conduct an optical test on the target photovoltaic surface, and photograph and extract an optical distribution map, which contains the light transmittance and refractive index of the target photovoltaic surface.

[0026] In the above step, a professional optical test device (spectrophotometer, refractive index measuring instrument) carried by the photovoltaic cleaning robot is used to measure the pre-positioned target photovoltaic surface. Specifically: adjust the spectrophotometer to an appropriate wavelength range so that the emitted light is evenly projected onto the target photovoltaic surface, and calculate the light transmittance data of each area by measuring the light intensity passing through the photovoltaic surface. At the same time, use the refractive index measuring instrument to measure the light refraction conditions at different positions of the target photovoltaic surface point by point, and then obtain the corresponding refractive index data. During the measurement process, scan the photovoltaic surface at a uniform and sufficiently fine interval to ensure that the entire target photovoltaic area is covered. After the measurement, process the discrete data of the collected light transmittance and refractive index, use the interpolation algorithm to convert the discrete data into continuous distribution information, and visually present the distribution of the light transmittance and refractive index with different colors or gray values through image processing technology, and finally generate an optical distribution map containing the light transmittance and refractive index information of the target photovoltaic surface.

[0027] S102. Determine the ice layer thickness distribution on the surface of the target photovoltaic surface according to the optical distribution map.

[0028] In the above step, first perform denoising processing on the optical distribution map, remove the obvious abnormal points in the optical distribution map, then calculate the average light transmittance of the optical distribution map, and finally use the Beer-Lambert law to calculate the ice layer thickness on the surface of the target photovoltaic surface; the calculation formula of the Beer-Lambert law is as follows: where T is the light transmittance, is the absorption coefficient of the ice layer to light, and d is the ice layer thickness; However, in the calculation formula of Beer-Lambert law it refers to the absorption coefficient of pure ice layer to light. In actual situation, the ice layer often contains impurities (such as salts, dust particles, etc.), which may lead to inaccurate calculation results of the ice layer. Therefore, in this application, the optical distribution map is first converted into an optical image matrix, which is composed of multiple pixel points, and each pixel point stores its corresponding refractive index and light transmittance. Then, the refractive index ice layer thickness of the current optical distribution map is calculated using the Beer-Lambert law calculation formula, and the light transmittance ice layer thickness of the current optical distribution map is calculated using the Snell's law calculation formula. It should be noted that if the ice layer is pure with extremely low impurity content, the calculated refractive index ice layer thickness and light transmittance ice layer thickness are very close. However, when the impurity content is relatively high, due to the different influencing degrees of impurities on the refractive index and light transmittance of the ice layer, the calculated refractive index ice layer thickness and light transmittance ice layer thickness will also show significant differences. According to this characteristic, this application uses the ice layer thickness difference between the refractive index ice layer thickness and the light transmittance ice layer thickness to determine the impurity content of the current ice layer. If the ice layer thickness difference is less than or equal to the ice layer thickness difference threshold, it indicates that the impurity content of the current ice layer is relatively low, and either the refractive index ice layer thickness or the light transmittance ice layer thickness at the pixel point can be selected as the ice layer thickness of this pixel point.

[0029] If the ice layer thickness difference is greater than the ice layer thickness difference threshold, gradient analysis needs to be performed on the optical image matrix to screen out the pure ice layer area, so as to improve the accuracy of the calculation results. Specifically: First, the optical image matrix is converted into a gradient matrix, then the refractive index gradient vector and the light transmittance gradient vector of each gradient point in the gradient matrix are calculated, and the Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient between the refractive index gradient vector and the light transmittance gradient vector within each gradient point. At this time, if the refractive index gradient vector and the light transmittance gradient vector of the gradient point are highly correlated, it indicates that the change trends of the refractive index and the light transmittance of this gradient point are highly consistent, and the influence of impurities on the refractive index and the light transmittance is small, which further indicates that the impurity content of this gradient point is relatively low. On the contrary, if the correlation between the refractive index gradient vector and the light transmittance gradient vector of the gradient point is low, it indicates that the influence of impurities on the refractive index and the light transmittance in this gradient point is large, and the impurity content is relatively high. Based on this characteristic, by introducing the correlation coefficient between the refractive index gradient vector and the light transmittance gradient vector into the weight function, the weight coefficient of each gradient point is determined, and then the weight coefficients of each gradient point are normalized so that the sum of all gradient points is 1. Finally, according to the weight coefficients of each gradient point, the ice layer thickness of each gradient point is calculated. Among them, the weight function is specifically: Among them, is the weight coefficient of the gradient point in the x-th row and y-th column of the gradient matrix, and r(x, y) is the correlation coefficient between the refractive index gradient vector and the transmittance gradient vector within the gradient point in the x-th row and y-th column of the gradient matrix. , , and are respectively the lateral refractive index gradient value, the longitudinal refractive index gradient value, the lateral transmittance gradient value, and the longitudinal transmittance gradient value of the gradient point in the x-th row and y-th column of the gradient matrix.

[0030] In the above formula, is used to represent the degree of change of the optical parameter in the lateral and longitudinal directions. If the calculated value is large, it indicates that the optical parameter changes violently around the pixel point, and the optical characteristics of the ice layer are unstable, that is, the finally calculated weight coefficient is small; then the expression form of the correlation coefficient is introduced. If the correlation is high in this expression form, then will be small, and the finally obtained weight coefficient will be large. In addition, the correlation coefficient can further amplify the degree of change of the optical parameter in the lateral and longitudinal directions, thereby further increasing the weight value of the gradient point with more impurities, so as to reduce the influence of impurities on the calculation result in the subsequent calculation process.

[0031] The calculation formula for the ice layer thickness is: where d is the ice layer thickness, is the refractive index ice layer thickness of the gradient point in the x-th row and y-th column of the gradient matrix, is the transmittance ice layer thickness of the gradient point in the x-th row and y-th column of the gradient matrix, is the weight coefficient of the gradient point in the x-th row and y-th column of the gradient matrix, and n is the total number of gradient points in the gradient image.

[0032] In the above formula, and are both the refractive index ice layer thickness and the transmittance ice layer thickness calculated by taking the current ice layer as a pure ice layer. However, affected by impurities, multiplying the two by their corresponding weight coefficients reduces the contribution degree of the ice layer thickness of the gradient point with a higher impurity content, so as to obtain the accurate ice layer thickness of the gradient point.

[0033] S103. Based on the ice layer thickness distribution, determine the ice melting strategy of the photovoltaic cleaning robot, and the ice melting strategy includes the ice melting temperature and the ice melting path.

[0034] In the above steps, it should be noted that compared with directly using hot water to melt the ice layer, the specific surface area of the atomized water vapor increases significantly, enabling it to come into full contact with the ice layer, evenly cover the surface of the ice layer, prevent local overheating or uneven heating, avoid thermal stress damage to the photovoltaic module, and at the same time ensure the complete melting of the ice layer. In addition, the temperature and flow rate of the atomized water vapor are easy to adjust, and can be flexibly adjusted according to the ice layer thickness and environmental conditions, achieving high efficiency and energy conservation.

[0035] During the ice melting process, the freshly melted ice water will flow downward along the surface of the photovoltaic panel towards the ground. Due to the relatively low external environmental temperature, the path area through which the melted ice water flows will refreeze, and at this time, it is necessary to re-melt the ice, thus affecting the overall ice melting efficiency. To solve this problem, in this application, based on the physical distances between multiple gradient points on the target photovoltaic panel and the ground, the path weights of multiple gradient points are determined. It can be understood that the farther the gradient point is from the ground, the longer the flow path on the surface of the photovoltaic panel, and the more it needs to be preferentially melted, so its path weight is greater. Then, multiply the path weights of multiple gradient points by their respective corresponding ice layer thicknesses to obtain the ice melting priorities of multiple gradient points. Here, it needs to be explained that for areas with a relatively thin ice layer thickness, since the amount of melted ice water is less and its impact is relatively small, even if it is far from the ground, its ice melting priority will be relatively low; while for areas with a relatively thick ice layer thickness, since the amount of melted ice water is more and its impact is relatively large, even if it is close to the ground, its ice melting priority will be relatively high. Finally, based on the ice melting priorities of multiple gradient points, the ice melting path is determined to improve the overall ice melting efficiency.

[0036] When using atomized water vapor to melt the ice layer, it can be understood that if the ice melting temperature is too low, the ice melting efficiency will be low; if the ice melting temperature is too high, it will cause a thermal shock to the photovoltaic panel. Therefore, in order to ensure efficient ice melting while avoiding damage to the photovoltaic panel, it is also very important to control its ice melting temperature. Therefore, when calculating the ice melting temperature of the atomized water vapor, this application first calculates the required ice melting heat of multiple gradient points based on the ice layer thicknesses of multiple gradient points, and then uses the heat balance conduction formula according to the environmental temperature and the required ice melting heat of multiple gradient points to calculate the ice melting temperatures of multiple gradient points. Specifically, the following formula can be used: Among them, is the ice melting temperature, is the environmental temperature, r is the radius of the water vapor particle, v is the velocity of the water vapor particle, d is the ice layer thickness, is the water vapor flow rate, and k is the ice melting heat balance constant.

[0037] In the above formula, when the heat provided by the atomized water vapor is exactly equal to the heat required for ice layer melting, which is the most efficient situation for ice melting efficiency and will not cause thermal shock to the photovoltaic surface. When the thickness of the ice layer is known, the total heat required for ice layer melting can be calculated using the heat of fusion formula of the ice layer. Then, according to the specified ice melting time in the actual situation, the required ice melting efficiency can be obtained. Finally, based on the heat balance transfer formula of ambient temperature - atomized water vapor - ice layer and the basic parameters of the current atomization device, the optimal air flow rate, air flow velocity, and water vapor particles are adjusted. Among them, the ice melting heat balance constant k integrates relatively fixed physical property parameters such as the density of water, the density of pure ice layer, the heat of fusion of pure ice layer, and the specific heat capacity of water vapor. The value of k remains constant, making the calculation more focused on the influence of variables on the ice melting temperature. Finally, based on these adjusted parameters, the most efficient ice melting temperature can be calculated.

[0038] S104. According to the ice melting strategy, control the photovoltaic cleaning machine to clean the target photovoltaic surface.

[0039] In the above steps, the control system of the photovoltaic cleaning robot adjusts the heating device according to the ice melting strategy to heat up or cool down the water used to generate atomized water vapor, so that the atomized water vapor meets the set temperature requirements. Then, the atomization device is started to convert the water meeting the temperature standard into fine water vapor particles. Finally, the atomized water vapor is sprayed onto the target photovoltaic surface in a uniform and stable manner through the nozzle assembly. It should be noted that during the spraying process, the photovoltaic cleaning robot will ensure that the atomized water vapor fully covers every part of the target photovoltaic surface according to the ice melting path, so as to reduce the difficulty of subsequent flushing and cleaning of the target photovoltaic surface.

[0040] In a possible implementation manner, after melting the ice layer, in order to further remove some stubborn dirt, at this time, atomized water vapor containing a decontamination component needs to be sprayed on the surface of the target photovoltaic module to reduce the viscosity of the stubborn dirt.

[0041] Refer to Figure 2 , this application also provides a cleaning control system for a photovoltaic cleaning robot. This system is the control system of the photovoltaic cleaning robot, including a test module 1, a processing module 2, and a control module 3, where: The test module 1 is used to perform optical tests on the target photovoltaic surface and capture and extract an optical distribution map, which includes the light transmittance and refractive index of the target photovoltaic surface; The processing module 2 is used to determine the ice layer thickness distribution on the surface of the target photovoltaic surface according to the optical distribution map; based on the ice layer thickness distribution, determine the ice melting strategy of the photovoltaic cleaning robot, and the ice melting strategy includes the ice melting temperature and the ice melting path; The control module 3 is used to control the photovoltaic cleaning machine to clean the target photovoltaic surface according to the ice melting strategy.

[0042] It should be noted that when the device provided in the above embodiments realizes its functions, only the division of the above function modules is used for illustration. In actual applications, the above functions can be allocated to different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0043] This application also discloses an electronic device. Referring to Figure 3 , Figure 3 FIG. is a schematic structural diagram of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0044] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0045] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0046] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0047] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by invoking the data stored in the memory 305, it executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0048] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , in the memory 305 as a computer storage medium, there may be included an operating system, a network communication module, a user interface module, and an application program for a cleaning control method of a photovoltaic cleaning robot.

[0049] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a cleaning control method of a photovoltaic cleaning robot. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the above embodiments. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0050] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0051] In several implementation manners provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect couplings or communication connections of the device or unit can be in electrical or other forms.

[0052] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0054] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0055] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the disclosure of the practical truth, those skilled in the art will easily think of other implementation manners of the present disclosure.

[0056] The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A cleaning control method for a photovoltaic cleaning robot, characterized in that: Applied to a photovoltaic cleaning robot control system, the method comprises: Performing an optical test on the target photovoltaic surface, and photographing and extracting an optical distribution map, wherein the optical distribution map includes the transmittance and refractive index of the target photovoltaic surface; Determining the distribution of ice thickness on the surface of the target photovoltaic surface according to the optical distribution map; Based on the ice layer thickness distribution, determining an ice melting strategy of the photovoltaic cleaning robot, wherein the ice melting strategy includes an ice melting temperature and an ice melting path; According to the ice melting strategy, the photovoltaic cleaning machine is controlled to clean the target photovoltaic surface.

2. The method according to claim 1, characterized in that Determining the ice thickness distribution on the target photovoltaic surface according to the optical distribution map specifically includes: Converting the optical distribution diagram into an optical image matrix, wherein the optical image matrix is ​​composed of a plurality of pixel points, and each of the pixel points stores its corresponding refractive index and transmittance; Calculating the refractive index ice layer thickness and the transmittance ice layer thickness of the optical image matrix; Calculating the ice layer thickness difference between the refractive index ice layer thickness and the transmittance ice layer thickness; Determining whether the ice layer thickness difference is less than or equal to an ice layer thickness difference threshold; If the ice layer thickness difference is less than or equal to the ice layer thickness difference threshold, any one of the refractive index ice layer thickness and the transmittance ice layer thickness is selected as the ice layer thickness distribution of the target photovoltaic surface.

3. The method according to claim 2, characterized in that The determining whether the ice layer thickness difference is less than or equal to an ice layer thickness difference threshold further includes: If the ice layer thickness difference is greater than the ice layer thickness difference threshold, converting the optical image matrix into a gradient matrix, the gradient matrix is ​​composed of a plurality of gradient points, wherein one gradient point corresponds to one pixel point; Calculating the transmittance gradient vector and the refractive index gradient vector of the plurality of gradient points; Calculating the transmittance-refractive index correlation coefficient corresponding to each of the plurality of gradient points according to the transmittance gradient vectors and the refractive index gradient vectors of the plurality of gradient points; Using a weight function, calculating ice thickness weight coefficients of a plurality of said gradient points; Based on the transmittance-refractive index correlation coefficient corresponding to each of the plurality of gradient points and the ice thickness weight coefficient, the ice thickness distribution on the target photovoltaic surface is calculated, and the ice thickness distribution includes the ice thickness corresponding to each of the plurality of gradient points.

4. The method according to claim 3, characterized in that The weight function is specifically: in, is the weight coefficient of the gradient point in the xth row and yth column in the gradient matrix, r(x, y) is the correlation coefficient between the refractive index gradient vector and the transmittance gradient vector in the gradient point in the xth row and yth column in the gradient matrix, , , and are the transverse gradient value of the refractive index, the longitudinal gradient value of the refractive index, the transverse gradient value of the transmittance, and the longitudinal gradient value of the transmittance at the gradient point in the xth row and the yth column in the gradient matrix respectively.

5. The method according to claim 3, characterized in that: Determining the ice melting strategy of the photovoltaic cleaning robot based on the ice thickness distribution specifically includes: Determining path weights of the plurality of gradient points according to the physical heights of the plurality of gradient points and the ground; Multiplying the path weights of the plurality of gradient points by the thickness of the ice layer corresponding to each of the plurality of gradient points to determine the ice melting priority of the plurality of gradient points; The ice melting path is determined according to the ice melting priorities of the plurality of gradient points.

6. The method according to claim 3, characterized in that The step of determining an ice melting strategy of the photovoltaic cleaning robot based on the ice thickness distribution specifically includes: Calculating the required ice melting heat at the plurality of gradient points according to the ice layer thickness at the plurality of gradient points; The ice melting temperatures of the plurality of gradient points are calculated based on the ambient temperature and the required ice melting heat of the plurality of gradient points using a heat balance conduction formula.

7. The method according to claim 1, characterized in that After controlling the photovoltaic cleaning machine to clean the target photovoltaic surface, the method further includes: When the ice layer on the target photovoltaic surface melts, the component list in the atomized water vapor is updated, and the component list contains stain removal components for stubborn stains.

8. A cleaning control system for a photovoltaic cleaning robot, characterized in that: The system is a photovoltaic cleaning robot control system, comprising a testing module (1), a processing module (2) and a control module (3), wherein: The test module (1) is used to perform an optical test on a target photovoltaic surface and to capture and extract an optical distribution diagram, wherein the optical distribution diagram contains the transmittance and refractive index of the target photovoltaic surface; The processing module (2) is used to determine the distribution of ice thickness on the target photovoltaic surface according to the optical distribution map; based on the distribution of ice thickness, determine the ice melting strategy of the photovoltaic cleaning robot, wherein the ice melting strategy includes ice melting temperature and ice melting path; The control module (3) is used to control the photovoltaic cleaning machine to clean the target photovoltaic surface according to the ice melting strategy.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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